Oxford Bulletin of Economics and Statistics

Publisher:: Wiley
Publication date:: 2021-02-01

ISBN:: 0305-9049

Issue Number

No. 86-1, February 2024

Latest documents

Non‐parametric Estimator for Conditional Mode with Parametric Features*
We in this paper propose a new approach for estimating conditional mode non‐parametrically to capture the ‘most likely’ effect built on local linear approximation, in which a parametric pilot modal regression is locally adjusted through a kernel smoothing fit to potentially reduce the bias asymptotically without affecting the variance of the estimator. Specifically, we first estimate a parametric modal regression utilizing prior information from initial studies or economic analysis, and then estimate the non‐parametric modal function based on the additive correction by eliminating the parametric feature. We derive the asymptotic normal distribution of the proposed modal estimator for both fixed and estimated parametric feature cases, and demonstrate that there is substantial room for bias reduction under certain regularity conditions. We numerically estimate the suggested modal regression model with the use of a modified modal‐expectation‐maximization (MEM) algorithm. Monte Carlo simulations and one empirical analysis are presented to illustrate the finite sample performance of the developed modal estimator. Several extensions, including multiplicative correction, generalized guidance, modal‐based robust regression and the incorporation of categorical covariates, are also discussed for the sake of completeness.
Smooth and Abrupt Dynamics in Financial Volatility: The MS‐MEM‐MIDAS*
In this paper, we maintain that the evolution of the realized volatility is characterized by a combination of high‐frequency dynamics and smoother, yet persistent, dynamics evolving at a lower frequency. We suggest a new Multiplicative Error Model which combines the mixed frequency features of a MIDAS at the monthly level with Markovian dynamics at the daily level. When estimated in‐sample on the realized kernel volatility of the S&P500 index, this model dominates other simpler specifications, especially when monthly aggregated realized volatility is used. The same pattern is confirmed in the out‐of‐sample forecasting performance which suggests that adding an abrupt change in the average level of volatility better helps in tracking quick bursts of volatility and a relatively rapid absorption of the shocks.
Identifying Politically Connected Firms: A Machine Learning Approach*
This article introduces machine learning techniques to identify politically connected firms. By assembling information from publicly available sources and the Orbis company database, we constructed a novel firm population dataset from Czechia in which various forms of political connections can be determined. The data about firms' connections are unique and comprehensive. They include political donations by the firm, having members of managerial boards who donated to a political party, and having members of boards who ran for political office. The results indicate that over 85% of firms with political connections can be accurately identified by the proposed algorithms. The model obtains this high accuracy by using only firm‐level financial and industry indicators that are widely available in most countries. These findings suggest that machine learning algorithms could be used by public institutions to improve the identification of politically connected firms with potentially large conflicts of interest.
Issue Information
Are Economics Conferences Gender‐Neutral? Evidence from Ireland*
We study gender inequality in conference acceptance using data from the Irish Economic Association annual conference from 2016 to 2022, exploiting the introduction of anonymized submission in 2021 to study the effect of blinding. While no gender gap is observed in organizers' acceptance decisions, there is an indication of gender difference favouring the in‐group at the reviewer stage. In particular, male reviewers persistently give higher scores to papers with an increasing share of male authors. Evidence suggests that the difference stems from unconscious stereotyping against lesser known female authors. Anonymization eliminates the gender gap of male reviewers, but introduces a gender gap in favour of male authors for female reviewers. We explore differential selection as an alternative explanation, finding that reviewer experience postblinding could potentially account for our results.
Sequencing the COVID‐19 Recession in the USA: What Were the Macroeconomic Drivers?
We apply a structural vectorautoregressive analysis to decompose fluctuations in the growth rate of industrial production and inflation precipitated by the COVID‐19 pandemic in the USA into aggregate demand, aggregate supply, and uncertainty shocks. While all three types of shocks contributed to output and inflation dynamics, the surge in economic uncertainty contributed to the decline in output more strongly than aggregate demand or aggregate supply disruptions. In 2021, the decline in uncertainty and adverse aggregate supply shocks emerged to be similarly important in spurring inflation.
Partial Identification of Marginal Treatment Effects with Discrete Instruments and Misreported Treatment*
This paper provides partial identification results for the marginal treatment effect (MTE) when the binary treatment variable is potentially misreported and the instrumental variable is discrete. Identification results are derived under smoothness assumptions. Bounds for both the case of misreported treatment and the case of no misreported treatment are derived. The identification results are illustrated by identifying the marginal treatment effects of food stamps on health.
Foetal Exposure to Air Pollution and Students' Cognitive Performance: Evidence from Agricultural Fires in Brazil*
This paper examines the impact of foetal exposure to air pollution from agricultural fires on Brazilian students' cognitive performance later in life. We rely on comparisons across children who were upwind and downwind of the fires while in utero to address concerns around sorting and temporary income shocks. Our findings show that agricultural fires increase PM2.5$$ {\mathrm{PM}}_{2.5} $$, resulting in significant negative effects on pupils' scores in Portuguese and Maths in the 5th$$ 5\mathrm{th} $$ grade through prenatal exposure. Back‐of‐the‐envelope calculations suggest that a 1% reduction in PM2.5$$ {\mathrm{PM}}_{2.5} $$ from agricultural burning has the potential to increase later life wages by 2.6%.
A Brief History of General‐to‐specific Modelling*
We review key stages in the development of general‐to‐specific modelling (Gets). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, Gets applications faced many criticisms, especially from accusations of ‘data mining’—no longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non‐stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables.
Job Polarization and the Declining Wages of Young Female Workers in the United Kingdom*
We examine whether the decline of routine occupations contributed to rising wage inequality between young and prime‐age non‐college educated women in the UK over 2001‐2019. We estimate age, period, and cohort effects for the likelihood of employment in different occupations and the wages earned therein. For recent generations, cohort effects indicate a higher likelihood of employment in low‐paying manual jobs relative to high‐paying abstract ones. Cohort effects also underpin falling wages for post‐1980 cohorts across all occupations. We find that the latter channel, rather than job polarization, has been the main driver of rising inter‐age inequality among non‐college females.